Efficacy of Probiotic, Chlorhexidine, and Sodium Fluoride Mouthrinses on Mutans Streptococci in 8- to 12-Year-Old Children: A Crossover Randomized Trial

<b><i>Introduction:</i></b> The oral cavity is home to a diverse and distinct microbiome. While the role of oral bacteria in cariogenic and other dental diseases is irrefutable, their beneficial effects in the form of probiotics (PB) has been less studied, especially pertaining to oral diseases in children. This study compares the efficacy of a PB mouthrinse with 0.12% chlorhexidine (CHX) and 0.05% sodium fluoride (NaF) mouthrinse on the colony counts of mutans streptococci (MS) in children. <b><i>Methods:</i></b> A triple-blind crossover randomized trial between interventional groups was planned. Fifty-one children between 8 to 12 years of age were divided into three groups (I, II, and III) and were exposed to all three mouthrinses (A, B, and C) by randomized allocation for a period of two weeks with an inter-phase washout period of four weeks. Pre- and post-interventional MS counts (CFU/mL) were assessed, and the mean change was analysed using the <i>t</i> test (intragroup) and ANOVA (intergroup and crossover). <b><i>Results:</i></b> The mean changes in the colony counts obtained with the use of PB, CHX, and NaF mouthrinses were −1.0223 (−1.2201 to −0.8246), −0.9564 (−1.1503 to −0.7626), and −0.9511 (−1.1554 to −0.7467), respectively, which were statistically significant (<i>p</i> &#x3c; 0.0001). However, the intergroup comparison for the mean change in colony counts revealed no statistically significant differences (<i>p</i> &#x3e; 0.05). <b><i>Conclusion:</i></b> The study concluded that the PB mouthrinse was equally efficacious as compared to CHX and NaF mouthrinses against MS in 8- to 12-year-old children. However, further studies are recommended to strengthen the evidence.

Association of age-related cataract risk high polygenetic risk scores involved in galactose-related metabolism and dietary interactions

Introduction: Cataracts are associated with the accumulation of galactose and galactitol in the lens. We determined the polygenetic risk scores for the best model(PRSBM) associated with age-related cataract(ARC) risk and their interaction with diets and lifestyles in 40,262 Korean adults aged over 50 years belonged to a hospital-based city cohort. Methods: The genetic variants for ARC risk were selected in lactose and galactose metabolism-related genes with multivariate logistic regression using the PLINK 1.9 version. PRSBM from the selected genetic variants was estimated by generalized multifactor dimensionality reduction (GMDR) after adjusting covariates. The interactions between the PRSBM and each lifestyle factor were determined to modulate ARC risk. Results: The genetic variants for ARC risk related to lactose- and galactose metabolism were SLC2A1_rs3729548, ST3GAL3_rs3791047, LCT_rs2304371, GALNT5_rs6728956, ST6GAL1_rs2268536, GALNT17_rs17058752, CSGALNACT1_rs1994788, GALNTL4_rs10831608, B4GALT6_rs1667288, and A4GALT_ rs9623659. In GMDR, the best model included all ten genetic variants. The highest odds ratio (OR) for a single SNP in the PRSBM was 1.26. However, subjects with a high-PRSBM had a higher ARC risk by 2.1-fold than a low-PRSBM after adjusting for covariates. Carbohydrate, dairy products, kimchi, and alcohol intake interacted with PRSBM for ARC risk: the participants with high-PRSBM had a much higher ARC risk than those with low-PRSBM when consuming diets with high carbohydrate and low dairy product and kimchi intake. However, only with low alcohol intake, the participants with high-PRSBM had a higher ARC risk than those with low-PRSBM. Conclusion: Adults aged >50 years having high-PRSBM may modulate dietary habits to reduce ARC risk.

APOE genotypes, lipid profiles and associated clinical markers in a Finnish population with cardiovascular disease risk factors

Introduction: APOE ɛ4 allele predisposes to high cholesterol and increases the risk for lifestyle-related diseases such as Alzheimer’s disease (AD) and cardiovascular diseases (CVD). The aim of this study was to analyse interrelationships of APOE genotypes with lipid metabolism and lifestyle factors in middle-aged Finns among whom the CVD risk factors are common. Methods: Participants (n=211) were analysed for APOE ε genotypes, physiological parameters and health- and diet-related plasma markers. Lifestyle choices were determined by a questionnaire. Results: APOE genotypes ε3/ε4 and ε4/ε4 (ε4 group) represented 34.1% of the participants. Genotype ε3/ε3 (ε3 group) frequency was 54.5%. Carriers of ε2 (ε2 group; ε2/ε2, ε2/ε3 and ε2/ε4) represented 11.4%; 1.9 % were of the genotype ε2/ε4. The LDL and total cholesterol levels were lower (P<0.05) in the ε2 carriers than in the ε3 or ε4 groups, while the ε3 and ε4 groups did not differ. Proportions of plasma saturated fatty acids were higher (P<0.01) and omega-6 fatty acids lower (P=0.01) in the ε2 carriers compared with the ε4 group. The ε2 carriers had a higher (P<0.05) percentage of 22:4n-6 and 22:5n-6 and a lower (P<0.05) percentage of 24:5n-3 and 24:6n-3 than individuals without the ε2 allele. Conclusions: The plasma fatty acid profiles in the ε2 group were characterised by higher SFA and lower omega-6 fatty acid proportions. Their lower cholesterol values indicated a lower risk for CVD compared with the ε4 group. A novel finding was that the ε2 carriers had different proportions of 22:4n-6, 22:5n-6, 24:5n-3 and 24:6n-3 than individuals without the ε2 allele. The significance of the differences in fatty acid composition remains to be studied.

Quantile-Dependent Heritability of Glucose, Insulin, Proinsulin, Insulin Resistance, and Glycated Hemoglobin

<b><i>Background:</i></b> “Quantile-dependent expressivity” is a dependence of genetic effects on whether the phenotype (e.g., insulin resistance) is high or low relative to its distribution. <b><i>Methods:</i></b> Quantile-specific offspring-parent regression slopes (β_OP) were estimated by quantile regression for fasting glucose concentrations in 6,453 offspring-parent pairs from the Framingham Heart Study. <b><i>Results:</i></b> Quantile-specific heritability (<i>h</i>²), estimated by 2β_OP/(1 + <i>r</i>_spouse), increased 0.0045 ± 0.0007 (<i>p</i> = 8.8 × 10⁻¹⁴) for each 1% increment in the fasting glucose distribution, that is, <i>h</i>² ± SE were 0.057 ± 0.021, 0.095 ± 0.024, 0.146 ± 0.019, 0.293 ± 0.038, and 0.456 ± 0.061 at the 10th, 25th, 50th, 75th, and 90th percentiles of the fasting glucose distribution, respectively. Significant increases in quantile-specific heritability were also suggested for fasting insulin (<i>p</i> = 1.2 × 10⁻⁶), homeostatic model assessment of insulin resistance (HOMA-IR, <i>p</i> = 5.3 × 10⁻⁵), insulin/glucose ratio (<i>p</i> = 3.9 × 10⁻⁵), proinsulin (<i>p</i> = 1.4 × 10⁻⁶), proinsulin/insulin ratio (<i>p</i> = 2.7 × 10⁻⁵), and glucose concentrations during a glucose tolerance test (<i>p</i> = 0.001), and their logarithmically transformed values. <b><i>Discussion/Conclusion:</i></b> These findings suggest alternative interpretations to precision medicine and gene-environment interactions, including alternative interpretation of reported synergisms between <i>ACE, ADRB3</i>, <i>PPAR-γ2</i>, and <i>TNF-α</i> polymorphisms and being born small for gestational age on adult insulin resistance (fetal origin theory), and gene-adiposity (<i>APOE, ENPP1, GCKR, IGF2BP2, IL-6, IRS-1, KIAA0280, LEPR, MFHAS1, RETN, TCF7L2</i>), gene-exercise (<i>INS</i>), gene-diet (<i>ACSL1</i>, <i>ELOVL6</i>, <i>IRS-1</i>, <i>PLIN</i>, <i>S100A9</i>), and gene-socioeconomic interactions.

Applications of Metabolomics to Precision Nutrition

<b><i>Background:</i></b> For thousands of years, disabilities due to nutrient deficiencies have plagued humanity. Rickets, scurvy, anemia, stunted growth, blindness, and mental handicaps due to nutrient deficiencies affected up to 1/10 of the world’s population prior to 1900. The discovery of essential amino acids, vitamins, and minerals, in the early 1900s, led to a fundamental change in our understanding of food and a revolution in human health. Widespread vitamin and mineral supplementation, the development of recommended dietary allowances, and the implementation of food labeling and testing along with significant improvements in food production and food quality have meant that nutrient-related disorders have almost vanished in the developed world. The success of nutritional science in preventing disease at a population-wide level is one of the great scientific triumphs of the 20th century. The challenge for nutritional science in the 21st century is to understand how to use nutrients and other food constituents to enhance human health or prevent disease at a more personal level. This is the primary goal of precision nutrition. <b><i>Summary:</i></b> Precision nutrition is an emerging branch of nutrition science that aims to use modern omics technologies (genomics, proteomics, and metabolomics) to assess an individual’s response to specific foods or dietary patterns and thereby determine the most effective diet or lifestyle interventions to prevent or treat specific diseases in that individual. Metabolomics is vital to nearly every aspect of precision nutrition. It can be used to comprehensively characterize the thousands of chemicals in foods, to identify food byproducts in human biofluids or tissues, to characterize nutrient deficiencies or excesses, to monitor biochemical responses to dietary interventions, to track long-term or short-term dietary habits, and to guide the development of nutritional therapies. In this review, we will describe how metabolomics has been used to advance the field of precision nutrition by providing some notable examples or use cases. First, we will describe how metabolomics helped launch the field of precision nutrition through the diagnosis and dietary therapy of individuals with inborn errors of metabolism. Next, we will describe how metabolomics is being used to comprehensively characterize the full chemical complexity of many key foods, and how this is revealing much more about nutrients than ever imagined. Third, we will describe how metabolomics is being used to identify food consumption biomarkers and how this opens the door to a more objective and quantitative assessments of an individual’s diet and their response to certain foods. Finally, we will describe how metabolomics is being coupled with other omics technologies to develop custom diets and lifestyle interventions that are leading to positive health benefits. <b><i>Key Message:</i></b> Metabolomics is vital to the advancement of nutritional science and in making the dream of precision nutrition a reality.

Dietary Intake and TCF7L2 rs7903146 T Allele Are Associated with Elevated Blood Glucose Levels in Healthy Individuals

<b><i>Introduction:</i></b> Type 2 diabetes (T2D) is a leading cause of global mortality with diet and genetics being considered amongst the most significant risk factors. Recently, studies have identified a single polymorphism of the <i>TCF7L2</i> gene (rs7903146) as the most important genetic contributor. However, no studies have explored this factor in a healthy population and using glycated haemoglobin (HbA1c), which is a reliable long-term indicator of glucose management. This study investigates the association of the genetic polymorphism rs7903146 and dietary intake with T2D risk in a population free of metabolic disease. <b><i>Methods:</i></b> T2D risk was assessed using HbA1c plasma concentrations and dietary intake via a validated Food Frequency Questionnaire in 70 healthy participants. <b><i>Results:</i></b> T allele carriers had higher HbA1c levels than the CC group (32.4 ± 7.2 mmol/mol vs. 30.3 ± 7.6 mmol/mol, <i>p</i> = 0.005). Multiple regression reported associations between diet, genotype and HbA1c levels accounting for 37.1% of the variance in HbA1c (adj. <i>R</i>² = 0.371, <i>p</i> &#x3c; 0.001). The following macronutrients, expressed as a median percentage of total energy intake (TEI) in the risk group, were positively associated with HbA1c concentration: carbohydrate (≥39% TEI, <i>p</i> &#x3c; 0.005; 95% CI 0.030/0.130) protein (≥21% TEI, <i>p</i> &#x3c; 0.005, 95% CI 0.034/0.141), monounsaturated (≥15% TEI <i>p</i> &#x3c; 0.05, 95% CI 0.006/0.163) and saturated fatty acids (≥13% TEI; <i>p</i> &#x3c; 0.05, 95% CI 0.036/0.188). <b><i>Conclusion:</i></b> Carriers of the T allele showed significantly higher levels of HbA1c compared to non-carriers. Dietary intake affected T2D risk to a greater extent than genetic effects of <i>TCF7L2</i>rs7903146 genotype in a healthy population. The study focus on healthy individuals is beneficial due to the applicability of findings for T2D screening.

Interactions of Carbohydrate Intake and Physical Activity with Regulatory Genes Affecting Glycaemia: A Food4Me Study Analysis

<b><i>Introduction:</i></b> Carbohydrate intake and physical activity are related to glucose homeostasis, both being influenced by individual genetic makeup. However, the interactions between these 2 factors, as affected by genetics, on glycaemia have been scarcely reported. <b><i>Objective:</i></b> We focused on analysing the interplay between carbohydrate intake and physical activity levels on blood glucose, taking into account a genetic risk score (GRS), based on SNPs related to glucose/energy metabolism. <b><i>Methods:</i></b> A total of 1,271 individuals from the Food4Me cohort, who completed the nutritional intervention, were evaluated at baseline. We collected dietary information by using an online-validated food frequency questionnaire, a questionnaire on physical activity, blood biochemistry by analysis of dried blood spots, and by analysis of selected SNPs. Fifteen out of 31 SNPs, with recognized participation in carbohydrate/energy metabolism, were included in the component analyses. The GRS included risk alleles involved in the control of glycaemia or energy-yielding processes. <b><i>Results:</i></b> Data concerning anthropometric, clinical, metabolic, dietary intake, physical activity, and genetics related to blood glucose levels showed expected trends in European individuals of comparable sex and age, being categorized by lifestyle, BMI, and energy/carbohydrate intakes, in this Food4Me population. Blood glucose was inversely associated with physical activity level (β = −0.041, <i>p</i> = 0.013) and positively correlated with the GRS values (β = 0.015, <i>p</i> = 0.047). Interestingly, an interaction affecting glycaemia, concerning physical activity level with carbohydrate intake, was found (β = −0.060, <i>p</i> = 0.033), which also significantly depended on the genetic background (GRS). <b><i>Conclusions:</i></b> The relationships of carbohydrate intake and physical activity are important in understanding glucose homeostasis, where a role for the genetic background should be ascribed.